DS 2026

Computational Probability

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Course Description

Covers the fundamentals of probability theory & stochastic processes. Become conversant in the tools of probability. Clearly describe & implement concepts related to random variables, properties of probability, distributions, expectations, moments, transformations, model fit, basic inference, sampling distributions, discrete & continuous time Markov chains, & Brownian motion. Illustrate most topics with both analytic & computational solutions.


  • Gianluca Guadagni

     Rating

     Difficulty

     GPA

    3.51

     Sections

    1

    Last Taught

    Fall 2025

  • Thomas Stewart

     Rating

    2.67

     Difficulty

    4.00

     GPA

    3.65

     Sections

    1

    Last Taught

    Fall 2025